Modelling Water Stress in a Shiraz Vineyard Using Hyperspectral Imaging and Machine Learning
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Site
2.2. Data Acquisition and Pre-Processing
2.3. Spectral Smoothing
2.4. Classification
2.4.1. Random Forest (RF)
2.4.2. Extreme Gradient Boosting (XGBoost)
2.5. Dimensionality Reduction
2.6. Accuracy Assessment
3. Results
3.1. Spectral Smoothing Using the Savitzky-Golay Filter
3.2. Important Waveband Selection
3.3. Classification Using Random Forest and Extreme Gradient Boosting
4. Discussion
4.1. Efficacy of the Savitzky-Golay Filter
4.2. Classification Using All Wavebands
4.3. Classification Using Subset of Important Wavebands
5. Conclusions
- Both RF and XGBoost may be utilised to model water stress in a Shiraz vineyard.
- Wavebands in the VIS region of the EM spectrum may be used to model water stress in a Shiraz vineyard.
- It is imperative that future studies carefully consider the impact of applying the Savitzky-Golay filter for smoothing spectral data.
- The developed framework requires further investigation to evaluate its robustness and operational capabilities.
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Parameter | Description | Default Value |
---|---|---|
max_depth | controls the maximum depth of each tree (used to control over-fitting) | 6 |
subsample | specifies the fraction of observations to be randomly sampled at each tree (adds randomness) | 1 |
eta | the learning rate | 0.3 |
nrounds | the number of trees to be produced (similar to ntree) | 100–1000 |
gamma | controls the minimum loss reduction required to make a node split (used to control over-fitting) | 0 |
min_child_weight | Specifies the minimum sum of instance weight of all the observations required in a child (used to control over-fitting) | 1 |
colsample_bytree | Specifies the number of features to consider when searching for the best node split (adds randomness) | 1 |
VIS (473 nm–680 nm) | Red-Edge (680 nm–708 nm) | |||
---|---|---|---|---|
RF | 12 | 474.74, 478.09, 478.94, 483.2, 494.64, 497.36, 500.11, 573.31, 574.59, 578.48, 579.79, 581.11 | 0 | - |
XGBoost | 9 | 520.31, 521.32, 524.36, 526.42, 541.34, 558.52, 564.56,630.23, 646.04 | 3 | 686.69, 698.39, 708.32 |
Overlap | 6 | 473.92, 480.63, 484.06 572.04, 577.17, 585.12 | 0 | - |
All Wavebands ( = 176) | Important Wavebands ( = 18) | ||||||||
---|---|---|---|---|---|---|---|---|---|
Train | Test | Train | Test | ||||||
Accuracy (%) | Kappa | Accuracy (%) | Kappa | Accuracy (%) | Kappa | Accuracy (%) | Kappa | ||
XGBoost | Unsmoothed | 85.0 | 0.70 | 78.3 | 0.57 | 90.0 | 0.80 | 80.0 | 0.60 |
Smoothed | 83.3 | 0.67 | 77.6 | 0.53 | 86.7 | 0.73 | 78.3 | 0.57 | |
RF | Unsmoothed | 90.0 | 0.80 | 83.3 | 0.67 | 93.3 | 0.87 | 83.3 | 0.67 |
Smoothed | 90.0 | 0.80 | 81.7 | 0.63 | 91.7 | 0.83 | 81.7 | 0.63 |
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Loggenberg, K.; Strever, A.; Greyling, B.; Poona, N. Modelling Water Stress in a Shiraz Vineyard Using Hyperspectral Imaging and Machine Learning. Remote Sens. 2018, 10, 202. https://doi.org/10.3390/rs10020202
Loggenberg K, Strever A, Greyling B, Poona N. Modelling Water Stress in a Shiraz Vineyard Using Hyperspectral Imaging and Machine Learning. Remote Sensing. 2018; 10(2):202. https://doi.org/10.3390/rs10020202
Chicago/Turabian StyleLoggenberg, Kyle, Albert Strever, Berno Greyling, and Nitesh Poona. 2018. "Modelling Water Stress in a Shiraz Vineyard Using Hyperspectral Imaging and Machine Learning" Remote Sensing 10, no. 2: 202. https://doi.org/10.3390/rs10020202
APA StyleLoggenberg, K., Strever, A., Greyling, B., & Poona, N. (2018). Modelling Water Stress in a Shiraz Vineyard Using Hyperspectral Imaging and Machine Learning. Remote Sensing, 10(2), 202. https://doi.org/10.3390/rs10020202